Extracting Semantic Concepts and Relations from Scientific Publications by Using Deep Learning

TL;DR

This paper proposes a deep learning approach to automatically extract semantic concepts and relations from scientific publications, aiming to improve ontology construction and address limitations of existing systems.

Contribution

It introduces new semantic relation types and leverages deep learning models for more comprehensive relation extraction from scientific texts.

Findings

Deep learning models effectively extract semantic relations.

New types of semantic relations are identified.

Improved coverage over existing ontology extraction methods.

Abstract

With the large volume of unstructured data that increases constantly on the web, the motivation of representing the knowledge in this data in the machine-understandable form is increased. Ontology is one of the major cornerstones of representing the information in a more meaningful way on the semantic Web. The current ontology repositories are quite limited either for their scope or for currentness. In addition, the current ontology extraction systems have many shortcomings and drawbacks, such as using a small dataset, depending on a large amount predefined patterns to extract semantic relations, and extracting a very few types of relations. The aim of this paper is to introduce a proposal of automatically extracting semantic concepts and relations from scientific publications. This paper suggests new types of semantic relations and points out of using deep learning (DL) models for semantic relation extraction.